Transcription of Poisson Models for Count Data
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Poisson Models for Count Data
Chapter 4 Poisson Models for CountDataIn this chapter we study log-linear Models for Count data under the assump-tion of a Poisson error structure. These Models have many applications, notonly to the analysis of counts of events, but also in the context of Models forcontingency tables and the analysis of survival Introduction to Poisson RegressionAs usual, we start by introducing an example that will serve to illustrativeregression Models for Count data. We then introduce the Poisson distributionand discuss the rationale for modeling the logarithm of the mean as a linearfunction of observed covariates.
number of trials where each speci c area has only a small probability of being hit. Assuming independence across days would lead to a binomial distribution which is well approximated by the Poisson. An alternative derivation of the Poisson distribution is in terms of a stochastic process described somewhat informally as follows. Suppose events
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